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This study included 368 anonymized UWF and 619 FP images. The ultra-widefield (UWF) images, which include both normal and pathologic retinal images, were based on Tsukazaki Optos Public Project. The traditional fundus photograph (FP) images were extracted from the publicly accessible database by using the Google image and Google dataset search that included English keywords related to retina. The search strategy was based on the following key terms: “fundus photography”, “retinal image”, and “fundus dataset”. The images were manually reviewed by two board-certified ophthalmologists, and blurred and low-quality images were removed to clarify the image domains. Duplicated images were also removed. Consequently, 368 images with artifacts and 619 images without artifacts were collected. The UWF images were cropped and masked after registration for CycleGAN.
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dataset including results of the Data in Brief "Data for functional MRI connectivity in transgender people with gender incongruence and cisgender individuals" supplementary data from the "Brain network interactions in transgender individuals with gender incongruence" manuscript submitted to Neuroimage journal Original T1- and T2*-weighted images can be found in: doi:10.17632/hjmfrv6vmg.1
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This data repository contains code and data for research article, namely, “Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting”, which will be (or have already been) published in the Journal of Hydrology. The streamflow data sets (daily streamflow series (01/01/1967-31/12/2014) of Yangxian station (yx), Han River and Zhangjiashan station (zjs), Jing River, China) used to build the proposed model are in the “time_series” directory. The fundamental code for decomposing streamflow data, deciding input predictors and output target, generating machine learning samples, building long short-term memory (LSTM) models and evaluating the model performance are organized in “tools” directory. The execution code for forecasting different streamflow series (zjs and yx) using different decomposition algorithms (e.g., variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), discrete wavelet transform (DWT) or non-decomposition-based (orig)) are organized in “projects” directory (e.g., “zjs_vmd/projects/”). To reproduce the results of this paper, follow the instructions given in “readme.md”. Note that the same results demonstrated in this paper cannot be reproduced but similar results should be reproduced.
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Gesture recognition technology is rapidly growing in the recent years due to the demands of many application such as computer game and sport, human robot interaction, assistant systems, sign language interpretation and e-commerce. One of the most important of gesture recognition is hand-gesture recognition. For example, it can be used to control all devices (television, radio, air-condition, and doors) by just hand gestures for smart home application. The HGM-4 dataset is built for hand gesture recognition (the full dataset is available from: http://dx.doi.org/10.17632/jzy8zngkbg.2) which contains total 4,160 colour images (1280 × 700 pixels) of 26 hand gestures captured by four cameras at different position. The training and testing set are defined to create a benchmark framework for comparing the experimental results.
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Files for Hardware design for build a Step Width System Capture
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Dataset aims for the simulation of the time evolution of binary adsorption system with the optical read out. It contains the code for generating three sequences and store them in Microsoft Excel file along with compressed results obtained by that code. The code is written for the MathWorks MATLAB or Octave environment. The first generated sequence is time, the second sequence is the number of adsorbed particles of one species of the binary mixture and the third sequence is the number of adsorbed particles of the other species of the binary mixture.
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  • Tabular Data
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Behavioral output from subjective ratings and memory response times during fMRI task. Output from a spatiotemporal and hippocampal-seed PLS analysis. Preprocessed fMRI contributing to the neuroimaging analysis.
Data Types:
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  • Software/Code
  • Tabular Data
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  • Text
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Open data for the manuscript "Ion beam synthesis and photoluminescence study of supersaturated fully-relaxed Ge-Sn alloys"
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Two sets of fused DEM generated by the proposed method correspond to two validation experiments.
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The GMT file and all the data files are available in each archive. The figure can therefore be easily reproduced.
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